Semi-global shape-aware attention network for image segmentation and retrieval | |
Zhang, Pengju3,4; Zhu, Jiagang1; Zhang, Chaofan2; Rong, Zheng4; Wu, Yihong3,4 | |
刊名 | NEUROCOMPUTING |
2022-09-28 | |
卷号 | 506 |
关键词 | Attention network Shape -awareness Semantic segmentation Image retrieval |
ISSN号 | 0925-2312 |
DOI | 10.1016/j.neucom.2022.07.069 |
通讯作者 | Zhang, Chaofan(zcfan@aiofm.ac.cn) ; Rong, Zheng(zheng.rong@nlpr.ia.ac.cn) ; Wu, Yihong(yhwu@nlpr.ia.ac.cn) |
英文摘要 | Non-local operations are usually used to capture long-range dependencies via aggregating global context to each position recently. However, most of the methods cannot preserve object shapes since they only focus on feature similarity but ignore proximity between central and other positions for capturing long-range dependencies, while shape-awareness is beneficial to many computer vision tasks. In this paper, we propose a Semi-Global Shape-aware Network (SGSNet) considering both feature similarity and prox-imity for preserving object shapes when modeling long-range dependencies. A hierarchical way is taken to aggregate global context. In the first level, each position in the whole feature map only aggregates con-textual information in vertical and horizontal directions according to both similarity and proximity. And then the result is input into the second level to do the same operations. By this hierarchical way, each central position gains supports from all other positions, and the combination of similarity and proximity makes each position gain supports mostly from the same semantic object. Moreover, we also propose an efficient algorithm for the aggregation of contextual information, where each of rows and columns in the feature map is treated as a binary tree to reduce similarity computation cost. Experiments on semantic segmentation and image retrieval show that adding SGSNet to existing networks gains solid improve-ments on both accuracy and efficiency.(c) 2022 Elsevier B.V. All rights reserved. |
资助项目 | National Natural Science Founda- tion of China[61836015] ; National Natural Science Founda- tion of China[62002359] ; National Natural Science Founda- tion of China[62102395] |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:000843462400012 |
资助机构 | National Natural Science Founda- tion of China |
内容类型 | 期刊论文 |
源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/132073] |
专题 | 中国科学院合肥物质科学研究院 |
通讯作者 | Zhang, Chaofan; Rong, Zheng; Wu, Yihong |
作者单位 | 1.XForwardAI Technol Co Ltd, Beijing, Peoples R China 2.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Anhui 230031, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Pengju,Zhu, Jiagang,Zhang, Chaofan,et al. Semi-global shape-aware attention network for image segmentation and retrieval[J]. NEUROCOMPUTING,2022,506. |
APA | Zhang, Pengju,Zhu, Jiagang,Zhang, Chaofan,Rong, Zheng,&Wu, Yihong.(2022).Semi-global shape-aware attention network for image segmentation and retrieval.NEUROCOMPUTING,506. |
MLA | Zhang, Pengju,et al."Semi-global shape-aware attention network for image segmentation and retrieval".NEUROCOMPUTING 506(2022). |
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